OpenRaiser/Intern-Atlas huggingface dataset:
Impact of COVID-19 on human mobility and retail sales in the US
Venue: arXiv cs.CY 2022
Year: 2022

Due to the COVID-19 pandemic, governments had to rapidly implement lockdown policies that restricted human mobility to suppress the spread of the disease and reduce mortality. Because of the movement restrictions resulting from government responses to the pandemic, US retail sales declined by -22% in April 2020 compared to the previous year. This study looks at the stringency of government policies, mobility patterns, and implied compliance levels. The relationships between these variables and the influence on retail sales serve to understand past human behavior and prepare for future pandemics. Retail losses varied dramatically across the US states, from -1.6% in Mississippi to -38.9% in Hawaii. States in the west and northeast were most affected, while those in the south were relatively resilient. Regression was used to identify statistically significant state-level characteristics. The greatest losses occurred in states with a high percentage of Democrat voters in the 2020 Presidential Election and those with large populations. A 10% increase in the Democrat vote is associated with a 2.4% increase in retail sales loss. States with a high percentage of adults with less than a high school diploma were most resilient. The number of trips of less than one-mile per capita is defined as the mobility index as it has the greatest influence on retail sales, on average, across the US states. An increase of 10% in this mobility index is associated with a 4.6% increase in retail sales. All states were generally compliant and exhibited reduced mobility with increasing stringency. A rise of 1% in the stringency index is associated with a decline of 1% in the mobility index. States with a high percentage of Democrat voters, large populations, and located in the west tend to be most compliant. A 10% rise in the proportion of people voting Democrat is associated with a 5% increase in compliance.
Induced Cycles and Paths Are Harder Than You Think
Venue: arXiv cs.CC 2022
Year: 2022

The goal of the paper is to give fine-grained hardness results for the Subgraph Isomorphism (SI) problem for fixed size induced patterns $H$, based on the $k$-Clique hypothesis that the current best algorithms for Clique are optimal. Our first main result is that for any pattern graph $H$ that is a {\em core}, the SI problem for $H$ is at least as hard as $t$-Clique, where $t$ is the size of the largest clique minor of $H$. This improves (for cores) the previous known results [Dalirrooyfard-Vassilevska W. STOC'20] that the SI for $H$ is at least as hard as $k$-clique where $k$ is the size of the largest clique {\em subgraph} in $H$, or the chromatic number of $H$ (under the Hadwiger conjecture). For detecting \emph{any} graph pattern $H$, we further remove the dependency of the result of [Dalirrooyfard-Vassilevska W. STOC'20] on the Hadwiger conjecture at the cost of a sub-polynomial decrease in the lower bound. The result for cores allows us to prove that the SI problem for induced $k$-Path and $k$-Cycle is harder than previously known. Previously [Floderus et al. Theor. CS 2015] had shown that $k$-Path and $k$-Cycle are at least as hard to detect as a $\lfloor k/2\rfloor$-Clique. We show that they are in fact at least as hard as $3k/4-O(1)$-Clique, improving the conditional lower bound exponent by a factor of $3/2$. Finally, we provide a new conditional lower bound for detecting induced $4$-cycles: $n^{2-o(1)}$ time is necessary even in graphs with $n$ nodes and $O(n^{1.5})$ edges.
PromptAttack: Prompt-based Attack for Language Models via Gradient Search
Venue: arXiv cs.CL 2022
Year: 2022

As the pre-trained language models (PLMs) continue to grow, so do the hardware and data requirements for fine-tuning PLMs. Therefore, the researchers have come up with a lighter method called \textit{Prompt Learning}. However, during the investigations, we observe that the prompt learning methods are vulnerable and can easily be attacked by some illegally constructed prompts, resulting in classification errors, and serious security problems for PLMs. Most of the current research ignores the security issue of prompt-based methods. Therefore, in this paper, we propose a malicious prompt template construction method (\textbf{PromptAttack}) to probe the security performance of PLMs. Several unfriendly template construction approaches are investigated to guide the model to misclassify the task. Extensive experiments on three datasets and three PLMs prove the effectiveness of our proposed approach PromptAttack. We also conduct experiments to verify that our method is applicable in few-shot scenarios.
Deciding a Graph Property by a Single Mobile Agent: One-Bit Memory Suffices
Venue: arXiv cs.DS 2022
Year: 2022

We investigate the computational power of the deterministic single-agent model where the agent and each node are equipped with a limited amount of persistent memory. Tasks are formalized as decision problems on properties of input graphs, i.e., the task is defined as a subset $\mathcal{T}$ of all possible input graphs, and the agent must decide if the network belongs to $\mathcal{T}$ or not. We focus on the class of the decision problems which are solvable in a polynomial number of movements, and polynomial-time local computation. The contribution of this paper is the computational power of the very weak system with one-bit agent memory and $O(1)$-bit storage (i.e. node memory) is equivalent to the one with $O(n)$-bit agent memory and $O(1)$-bit storage. We also show that the one-bit agent memory is crucial to lead this equivalence: There exists a decision task which can be solved by the one-bit memory agent but cannot be solved by the zero-bit memory (i.e., oblivious) agent. Our result is deduced by the algorithm of simulating the $O(n)$-bit memory agent by the one-bit memory agent with polynomial-time overhead, which is developed by two novel technical tools. The first one is a dynamic $s$-$t$ path maintenance mechanism which uses only $O(1)$-bit storage per node. The second one is a new lexicographically-ordered DFS algorithm for the mobile agent system with $O(1)$-bit memory and $O(1)$-bit storage per node. These tools are of independent interest.
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
Venue: arXiv cs.LG 2022
Year: 2022

Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researchers propose constructing a score function that satisfies the orthogonal condition, which guarantees that the established ATE estimator is "orthogonal" to be more robust. The others explore representation learning models to achieve a balanced representation between the treated and the controlled groups. However, existing studies fail to 1) discriminate treated units from controlled ones in the representation space to avoid the over-balanced issue; 2) fully utilize the "orthogonality information". In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory. This framework protects the representation from being over-balanced via multi-task learning. Simultaneously, MBRL incorporates the noise orthogonality information in the training and validation stages to achieve a better ATE estimation. The comprehensive experiments on benchmark and simulated datasets show the superiority and robustness of our method on treatment effect estimations compared with existing state-of-the-art methods.
Inferring Region Types via an Abstract Notion of Environment Transformation
Venue: arXiv cs.PL 2022
Year: 2022

Region-based type systems are a powerful tool for various kinds of program analysis. We introduce a new inference algorithm for region types based on an abstract notion of environment transformation. It analyzes the code of a method only once, even when there are multiple invocations of the method of different region types in the program. Elements of such an abstract transformation are essentially constraints for equality and subtyping that capture flow information of the program. In particular, we work with access graphs in the definition of abstract transformations to guarantee the termination of the inference algorithm, because they provide a finite representation of field access paths.
Impact analysis of recovery cases due to COVID19 using LSTM deep learning model
Venue: arXiv cs.LG 2022
Year: 2022

The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and do not know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM (Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
High Speed Rotation Estimation with Dynamic Vision Sensors
Venue: arXiv cs.CV 2022
Year: 2022

Rotational speed is one of the important metrics to be measured for calibrating the electric motors in manufacturing, monitoring engine during car repairing, faults detection on electrical appliance and etc. However, existing measurement techniques either require prohibitive hardware (e.g., high-speed camera) or are inconvenient to use in real-world application scenarios. In this paper, we propose, EV-Tach, an event-based tachometer via efficient dynamic vision sensing on mobile devices. EV-Tach is designed as a high-fidelity and convenient tachometer by introducing dynamic vision sensor as a new sensing modality to capture the high-speed rotation precisely under various real-world scenarios. By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets. According to our extensive evaluations, the Relative Mean Absolute Error (RMAE) of EV-Tach is as low as 0.03% which is comparable to the state-of-the-art laser tachometer under fixed measurement mode. Moreover, EV-Tach is robust to subtle movement of user's hand, therefore, can be used as a handheld device, where the laser tachometer fails to produce reasonable results.
Understanding Skills for OSS Communities on GitHub
Venue: arXiv cs.SE 2022
Year: 2022

The development of open source software (OSS) is a broad field which requires diverse skill sets. For example, maintainers help lead the project and promote its longevity, technical writers assist with documentation, bug reporters identify defects in software, and developers program the software. However, it is unknown which skills are used in OSS development as well as OSS contributors' general attitudes towards skills in OSS. In this paper, we address this gap by administering a survey to a diverse set of 455 OSS contributors. Guided by these responses as well as prior literature on software development expertise and social factors of OSS, we develop a model of skills in OSS that considers the many contexts OSS contributors work in. This model has 45 skills in the following 9 categories: technical skills, working styles, problem solving, contribution types, project-specific skills, interpersonal skills, external relations, management, and characteristics. Through a mix of qualitative and quantitative analyses, we find that OSS contributors are actively motivated to improve skills and perceive many benefits in sharing their skills with others. We then use this analysis to derive a set of design implications and best practices for those who incorporate skills into OSS tools and platforms, such as GitHub.
Compression Optimality of Asymmetric Numeral Systems
Venue: arXiv cs.IT 2022
Year: 2022

Compression also known as entropy coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming for instance) renews an interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from a beautiful mathematical structure, it is very efficient and offers compression with a very low residual redundancy. ANS works well for any symbol source statistics. Besides, ANS has become a preferred compression algorithm in the IT industry. However, designing ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression rate. The paper investigates compression optimality of ANS. It shows that ANS is optimal (i.e. the entropies of encoding and source are equal) for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to determine ANS compression rate precisely. We present two algorithms for finding ANS instances with high compression rates. The first explores state probability approximations in order to choose ANS instances with better compression rates. The second algorithm is a probabilistic one. It finds ANS instances, whose compression rate can be made as close to the best rate as required. This is done at the expense of the number $θ$ of internal random ``coin'' tosses. The algorithm complexity is ${\cal O}(θL^3)$, where $L$ is the number of ANS states. The complexity can be reduced to ${\cal O}(θL\log{L})$ if we use a fast matrix inversion. If the algorithm is implemented on quantum computer, its complexity becomes ${\cal O}(θ(\log{L})^3)$.
Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents
Venue: arXiv cs.LG 2022
Year: 2022

We present an approach for interpreting a black-box alarming system for forecasting accidents and anomalies during the drilling of oil and gas wells. The interpretation methodology aims to explain the local behavior of the accident predictive model to drilling engineers. The explanatory model uses Shapley additive explanations analysis of features, obtained through Bag-of-features representation of telemetry logs used during the drilling accident forecasting phase. Validation shows that the explanatory model has 15% precision at 70% recall, and overcomes the metric values of a random baseline and multi-head attention neural network. These results justify that the developed explanatory model is better aligned with explanations of drilling engineers, than the state-of-the-art method. The joint performance of explanatory and Bag-of-features models allows drilling engineers to understand the logic behind the system decisions at the particular moment, pay attention to highlighted telemetry regions, and correspondingly, increase the trust level in the accident forecasting alarms.
Wavelet-based Loss for High-frequency Interface Dynamics
Venue: arXiv cs.LG 2022
Year: 2022

Generating highly detailed, complex data is a long-standing and frequently considered problem in the machine learning field. However, developing detail-aware generators remains an challenging and open problem. Generative adversarial networks are the basis of many state-of-the-art methods. However, they introduce a second network to be trained as a loss function, making the interpretation of the learned functions much more difficult. As an alternative, we present a new method based on a wavelet loss formulation, which remains transparent in terms of what is optimized. The wavelet-based loss function is used to overcome the limitations of conventional distance metrics, such as L1 or L2 distances, when it comes to generate data with high-frequency details. We show that our method can successfully reconstruct high-frequency details in an illustrative synthetic test case. Additionally, we evaluate the performance when applied to more complex surfaces based on physical simulations. Taking a roughly approximated simulation as input, our method infers corresponding spatial details while taking into account how they evolve. We consider this problem in terms of spatial and temporal frequencies, and leverage generative networks trained with our wavelet loss to learn the desired spatio-temporal signal for the surface dynamics. We test the capabilities of our method with a set of synthetic wave function tests and complex 2D and 3D dynamics of elasto-plastic materials.
Let's Learn from Children: Scaffolding to Enable Search as Learning in the Educational Environment
Venue: arXiv cs.IR 2022
Year: 2022

In this manuscript, we argue for the need to further look at search as learning (SAL) with children as the primary stakeholders. Inspired by how children learn and considering the classroom (regardless of the teaching modality) as a natural educational ecosystem, we posit that scaffolding is the tie that can simultaneously allow for learning to search while searching for learning. The main contribution of this work is a list of open challenges focused on the primary school classroom for the IR community to consider when setting up to explore and make progress on SAL research with and for children and beyond.
ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion
Venue: arXiv cs.AI 2022
Year: 2022

Knowledge Graph Embedding (KGE) methods have gained enormous attention from a wide range of AI communities including Natural Language Processing (NLP) for text generation, classification and context induction. Embedding a huge number of inter-relationships in terms of a small number of dimensions, require proper modeling in both cognitive and computational aspects. Recently, numerous objective functions regarding cognitive and computational aspects of natural languages are developed. Among which are the state-of-the-art methods of linearity, bilinearity, manifold-preserving kernels, projection-subspace, and analogical inference. However, the major challenge of such models lies in their loss functions that associate the dimension of relation embeddings to corresponding entity dimension. This leads to inaccurate prediction of corresponding relations among entities when counterparts are estimated wrongly. ProjE KGE, published by Bordes et al., due to low computational complexity and high potential for model improvement, is improved in this work regarding all translative and bilinear interactions while capturing entity nonlinearity. Experimental results on benchmark Knowledge Graphs (KGs) such as FB15K and WN18 show that the proposed approach outperforms the state-of-the-art models in entity prediction task using linear and bilinear methods and other recent powerful ones. In addition, a parallel processing structure is proposed for the model in order to improve the scalability on large KGs. The effects of different adaptive clustering and newly proposed sampling approaches are also explained which prove to be effective in improving the accuracy of knowledge graph completion.
Butterflies: A new source of inspiration for futuristic aerial robotics
Venue: arXiv cs.RO 2022
Year: 2022

Nature is an inhabitant for enormous number of species. All the species do perform complex activities with simple and elegant rules for their survival. The property of emergence of collective behavior is remarkably supporting their activities. One form of the collective behaviour is the swarm intelligence -- all agents poses same rules and capabilities. This equality along with local cooperation in the agents tremendously leads to achieving global results. Some of the swarm behaviours in the nature includes birds formations , fish school maneuverings, ants movement. Recently, one school of research has studied these behaviours and proposed artificial paradigms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Glowworm Swarm Optimization (GSO) etc. Another school of research used these models and designed robotic platforms to detect (locate) multiple signal sources such as light, fire, plume, odour etc. Kinbots platform is one such recent experiment. In the same line of thought, this extended abstract presents the recently proposed butterfly inspired metaphor and corresponding simulations, ongoing experiments with outcomes.
Getting Users Smart Quick about Security: Results from 90 Minutes of Using a Persuasive Toolkit for Facilitating Information Security Problem Solving by Non-Professionals
Venue: arXiv cs.CY 2022
Year: 2022

There is a conflict between the need for security compliance by users and the fact that commonly they cannot afford to dedicate much of their time and energy to that security. A balanced level of user engagement in security is difficult to achieve due to difference of priorities between the business perspective and the security perspective. We sought to find a way to engage users minimally, yet efficiently, so that they would both improve their security awareness and provide necessary feedback for improvement purposes to security designers. We have developed a persuasive software toolkit to engage users in structured discussions about security vulnerabilities in their company and potential interventions addressing these. In the toolkit we have adapted and integrated an established framework from conventional crime prevention. In the research reported here we examine how non-professionals perceived security problems through a short-term use of the toolkit. We present perceptions from a pilot lab study in which randomly recruited participants had to analyze a crafted insider threat problem using the toolkit. Results demonstrate that study participants were able to successfully identify causes, propose interventions and engage in providing feedback on proposed interventions. Subsequent interviews show that participants have developed greater awareness of information security issues and the framework to address these, which in a real setting would lead ultimately to significant benefits for the organization. These results indicate that when well-structured such short-term engagement is sufficient for users to meaningfully take part in complex security discussions and develop in-depth understanding of theoretical principles of security.
Efficient quantum non-fungible tokens for blockchain
Venue: arXiv cs.CR 2022
Year: 2022

Blockchain is a decentralized system that allows transaction transmission and storage according to the roles of the Consensus algorithm and Smart contracts. Non-fungible tokens (NFTs) consolidate the best characteristics of blockchain technology to deliver unique and bona fide tokens, each with distinctive attributes with non-fungible resources. Unfortunately, current classical NFTs are suffering from high costs regarding the consumed power of mining and lack of security. Therefore, this paper presents a new protocol for preparing quantum non-fungible tokens where a quantum state representing NFT is mounted on a blockchain instead of physically giving it to the owner. The proposed scheme is simulated and analyzed against various attacks and proves its ability to secure against them. Furthermore, the presented protocol provides reliable and cheaper NFTs than the classical one.